不训练、无提示，用大模型自动纠正中文错字
A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models
- 直接用大模型逐字生成，靠发音/字形相似性修正错字
- 在5个数据集上表现超越主流通用纠错模型
- 适合快速部署的零样本纠错场景
本文提出一种无需训练、无需提示的中文错别字纠正方法，完全不同于以往所有方法。核心思路是将大语言模型（LLM）作为纯语言模型使用：从输入句首开始逐词生成，在每一步基于已生成部分预测下一个词的分布。为确保输出与原句语义一致，设计了最小畸变模型，利用原字与替换字间的发音或字形相似性进行约束。同时提出两种有效奖励策略，解决中文纠错中的实际挑战。在五个公开数据集上的实验表明，该方法显著提升了大模型性能，使其能够与当前最优的通用型中文错字纠正模型相媲美。
原文摘要 · Abstract (English)
This work proposes a simple training-free prompt-free approach to leverage large language models (LLMs) for the Chinese spelling correction (CSC) task, which is totally different from all previous CSC approaches. The key idea is to use an LLM as a pure language model in a conventional manner. The LLM goes through the input sentence from the beginning, and at each inference step, produces a distribution over its vocabulary for deciding the next token, given a partial sentence. To ensure that the output sentence remains faithful to the input sentence, we design a minimal distortion model that utilizes pronunciation or shape similarities between the original and replaced characters. Furthermore, we propose two useful reward strategies to address practical challenges specific to the CSC task. Experiments on five public datasets demonstrate that our approach significantly improves LLM performance, enabling them to compete with state-of-the-art domain-general CSC models.
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